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XKDD 2020 : 2nd International Workshop on eXplainable Knowledge Discovery in Data Mining


When Sep 14, 2020 - Sep 18, 2020
Where Ghent, Belgium
Submission Deadline Jun 11, 2020
Notification Due Jul 7, 2020
Final Version Due Jul 21, 2020
Categories    explainability   interpretable machine learning   transparent data mining   ethics, fairness and privacy

Call For Papers

XKDD 2020 - Call for Papers
2nd International Workshop on eXplainable Knowledge Discovery in Data Mining

Deadline Extension!!! New deadline is 11 June 2020.

In line with the organization of ECML-PKDD 2020, also the organizers of XKDD 2020 are working on COVID-19 contingency plans.
The workshop will not be postponed: it will take place as planned.
Thus, all accepted contributions will be published, presented, etc, as normal (although presentations might take a different form).
We are developing plans in case the conference needs to be organised virtually.
For sure it will be possible to participate online and provide video for replacing online talks when they are not possible.
Final decisions on the modalities will be taken and communicated as soon as possible, and in any case before the early registration deadline.


In the past decade, machine learning based decision systems have been widely used in a plethora of applications ranging from credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the application of these systems may bring myriad benefits, their use might involve some ethical and legal risks, such as codifying biases; jeopardizing transparency and privacy, reducing accountability. Unfortunately, these risks increase and are made more serious by the opacity of these systems, which often are complex and their internal logic is usually inaccessible to humans.

Nowadays most of the Artificial Intelligence (AI) systems are based on machine learning algorithms. The relevance and need of ethics in AI is supported and highlighted by the various initiatives that in the world provide recommendations and guidelines in the direction of making AI-based decision systems explainable and compliant with legal and ethical issues. These include the EU's GDPR regulation which introduces, to some extent, a right for all individuals to obtain ``meaningful explanations of the logic involved'' when automated decision making takes place, the ``ACM Statement on Algorithmic Transparency and Accountability'', the Informatics Europe's ``European Recommendations on Machine-Learned Automated Decision Making'' and ``The ethics guidelines for trustworthy AI'' provided by the  EU High-Level Expert Group on AI.

The challenge to design and develop trustworthy AI-based decision systems is still open and requires a joint effort across technical, legal, sociological and ethical domains.

The purpose of XKDD, eXaplaining Knowledge Discovery in Data Mining, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning. The workshop will seek top-quality submissions addressing uncovered important issues related to ethical, explainable and transparent data mining and machine learning. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. XKDD asks for contributions from researchers, academia, and industries, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective.

Topics of interest include, but are not limited to:


- Explainable Artificial Intelligence
- Interpretable Machine Learning
- Transparent Data Mining
- Explainability in Clustering Analysis
- Technical Aspects of Algorithms for Explanation
- Explaining Black Box Decision Systems
- Adversarial Attack-based Models
- Counterfactual and Prototype-based Explanations
- Causal Discovery for Machine Learning Explanation
- Fairness Checking
- Fair Machine Learning
- Explanation for Privacy Risk
- Ethics Discovery for Explainable AI
- Privacy-Preserving Explanations
- Transparent Classification Approaches
- Anonymity and Information Hiding Problems in Comprehensible Models
- Case Study Analysis
- Experiments on Simulated and Real Decision Systems
- Monitoring and Understanding System Behavior
- Privacy Risk Assessment
- Privacy by Design Approaches for Human Data
- Statistical Aspects, Bias Detection and Causal Inference
- Explanation, Accountability and Liability from an Ethical and Legal Perspective
- Benchmarking and measuring explanation
- Visualization-based explanations
- Iterative dialogue explanations


All contributions will be reviewed by at least three members of the Program Committee. As regards size, contributions can be up to 16 pages in LNCS format, i.e., the ECML PKDD 2020 submission format. All papers should be written in English and be in LNCS format. The following kinds of submissions will be considered: research papers, tool papers, case study papers, and position papers. Detailed information on the submission procedure are available at the workshop web page:

Accepted papers will be published after the workshop by Springer in a volume of Lecture Notes in Computer Science (LNCS). Condition for inclusion in the post-proceedings is that at least one of the co-authors has presented the paper at the workshop. Pre-proceedings will be available online before the workshop. We also allow accepted papers to be presented without publication in the conference proceedings, if the authors choose to do so. Some of the full paper submissions may be accepted as short papers after review by the Program Committee. A special issue of a relevant international journal with extended versions of selected papers is under consideration.

The submission link is the following:


Paper Submission deadline: Thursday, 11 June, 2020
Accept/Reject Notification: Tuesday, 7 July, 2020
Camera-ready deadline: Tuesday, 21 July, 2020
Workshop: Monday, 14 September, 2020


* Riccardo Guidotti, University of Pisa, Italy
* Anna Monreale, University of Pisa, Italy
* Salvatore Rinzivillo, ISTI-CNR, Pisa,  Italy
* Przemyslaw Biecek, Warsaw University of Technology, Poland

* Francesco Bonchi, ISI Foundation, Italy
* Christoph Molnar, Ludwig-Maximilians-University of Munich, Germany


Osbert Bastani, University of Pennsylvania, US
Livio Bioglio, University of Turin, Italy
Tobias Blanke, King's College London, UK
Francesco Bonchi, ISI Foundation, Italy
Giuseppe Casalicchio, Ludwig-Maximilians-University of Munich, Germany
Chaofan Chen, Duke University, UK
Luca Costabello, Accenture Labs Dublin, Ireland
Mark Coté, King's College London, UK
Miguel Couceiro, LORIA CNRS, France
Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
Boxiang Dong, Montclair State University, US
Alex Freitas, University of Kent, US
Luis Galárraga, Aalborg University, France
Giannotti Fosca, ISTI-CNR Pisa, Italy
Aristides Gionis, KTH Royal Institute of Technology, Sweden
Thibault Laugel, Sorbonne University, France
Paulo Lisboa, Liverpool John Moores University, UK
Pasquale Minervini, University College London, UK
Ioannis Mollas, Aristotle University of Thessaloniki, Greece
Christoph Molnar, Ludwig-Maximilians-University of Munich, Germany
Cecilia Panigutti, Scuola Normale Superiore Pisa, Italy
András Pataricza, Technical University of Budapest, Hungary
Dino Pedreschi, University of Pisa, Italy
Francesca Pratesi, University of Pisa, Italy
Xavier Renard, AXA - LinkedIn, Frances
Fabrizio Sebastiani, ISTI-CNR, Italy
Dylan Slack, University of California Irvine, US
Dominik Slezak, University of Warsaw, Poland
Vicenc Torra, Umeå University, Sweden
Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
Franco Turini, University of Pisa, Italy
Cagatay Turkay, University of Warwick, UK


All inquires should be sent to

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